Machine Learning and Human Learning
Explore the intersection of AI and education in this course on machine learning, human cognition, and educational technology.
Explore the intersection of AI and education in this course on machine learning, human cognition, and educational technology.
Dive into the fascinating world of machine learning and its impact on human education with this comprehensive course. Examine the key differences between machine and human learning, exploring both technical aspects of AI and broader philosophical questions about intelligence. Gain insights into practical applications of learning analytics and AI in educational tools, while critically evaluating their implications. Designed for educators and AI enthusiasts alike, this course offers a unique perspective on the future of learning in the age of artificial intelligence.
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What you'll learn
Understand the fundamental differences between machine and human learning
Explore technical definitions of supervised and unsupervised machine learning
Analyze the concept of mechanical intelligence and its relation to human cognition
Examine practical applications of learning analytics in educational tools
Critically evaluate the implementation of AI in education
Investigate cyber-social perspectives on intelligence and learning
Skills you'll gain
This course includes:
4.75 Hours PreRecorded video
13 peer reviews
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course explores the complex relationship between machine learning and human learning, focusing on their applications in education. Participants will examine technical aspects of supervised and unsupervised machine learning, as well as broader concepts of artificial intelligence. The curriculum covers key topics such as cyber-social perspectives, educational data mining, and the practical applications of AI in learning management systems. Through a blend of theoretical study and practical analysis, students will gain a comprehensive understanding of how AI is shaping the future of education and cognitive science.
Course Orientation + Differences between human and machine learning
Module 1 · 9 Hours to complete
Cyber Social Perspectives
Module 2 · 11 Hours to complete
Educational Data Mining
Module 3 · 4 Hours to complete
Framing the AI Discussion
Module 4 · 10 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Professor in Educational Policy Studies at the University of Illinois Urbana-Champaign
Dr. Bill Cope is a Professor in the Department of Educational Policy Studies at the University of Illinois Urbana-Champaign. He serves as the Principal Investigator on several significant projects funded by the Institute of Educational Sciences within the U.S. Department of Education and the Bill and Melinda Gates Foundation, focusing on the research and development of educational technologies. From 2010 to 2013, he held the position of Chair of the Journals Publication Committee for the American Educational Research Association. His recent publications include The Future of the Academic Journal (co-edited with Angus Phillips, Chandos, Oxford, 2009/2nd edition 2014) and Towards a Semantic Web: Connecting Knowledge in Academic Research (co-authored with Mary Kalantzis and Michael Magee, Woodhead, Cambridge, 2010). Dr. Cope holds one patent and has two pending patents in e-learning and web publishing. Alongside Mary Kalantzis, he co-authored New Learning: Elements of a Science of Education (Cambridge University Press, 2008/2nd edition 2012) and Literacies (Cambridge University Press, 2012), and co-edited Ubiquitous Learning (University of Illinois Press, 2009). For more information about his work, visit his research website at newlearningonline.com.
Instructor and Researcher at the University of Illinois Urbana-Champaign
Dr. Vania Carvalho de Castro is an instructor at the University of Illinois Urbana-Champaign, where she teaches the online course "Machine Learning and Human Learning" on Coursera. She currently holds the Werner Baer Post-Doctoral Position at the Lemann Center for Brazilian Studies, focusing on developing frameworks to assist Brazilian teachers in implementing the Base Nacional Comum Curricular (BNCC) in English education. Dr. Carvalho de Castro earned her Ph.D. in Applied Linguistics from the Federal University of Minas Gerais in Brazil in May 2021. Her research interests include emerging technologies in education, mobile learning, multiliteracies, teacher education, and artificial intelligence. She has previously worked as a visiting scholar at UIUC and as a Fulbright scholar at Florida State University. Dr. Carvalho de Castro's work emphasizes the integration of technology in educational practices to support teachers and students, particularly in underprivileged areas.
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